Method of estimating the state of charge of a battery and system thereof

ABSTRACT

The invention relates to a method for estimating the state of charge (SOC) of a battery when battery is in the at least states of: charging, discharging, and relaxing. The invention makes use of the battery voltage (VBAT) instead of the battery current. In order to build models in the method, we use standard charging and discharging processes to collect battery information.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of U.S. Ser. No.14/798,862, filed on Jul. 14, 2015, which is a continuation-in-partapplication of U.S. Ser. No. 14/617,982, filed on Feb. 10, 2015.

BACKGROUND

The battery state of charge (SOC) is essential information for the usersof portable electronic devices. The SOC of a fully-charged batteryrefers to 100%; the SOC of a fully-discharged battery would be 0%. Thereis a need for estimating the SOC by using embedded methods within theportable electronic devices.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are best understood from the followingdetailed description when read with the accompanying figures. It isnoted that, in accordance with the standard practice in the industry,various features are not drawn to scale. In fact, the dimensions of thevarious features may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a block diagram of an exemplary block diagram of a method ofestimating the state of charge of a battery in accordance with someembodiments.

FIG. 2 is a measurement result for building the weighting fuzzifier anddSOC/dV fuzzifier in the method of estimating the state of charge of abattery in accordance with some embodiments.

FIG. 3 shows one part of the model establishment for the dSOC/dVfuzzifier 120 in accordance with some embodiments.

FIG. 4 shows another part of the model establishment for the dSOC/dVfuzzifier 120 in FIG. 1 in accordance with some embodiments.

FIG. 5 shows an exemplary model of the dSOC/dV fuzzifier 120 in FIG. 1in accordance with some embodiments.

FIG. 6 shows the model establishment for the weighting fuzzifier 110 inFIG. 1 in accordance with some embodiments.

FIG. 7 shows a block diagram and an exemplary data table of an exemplaryblock diagram of a method of estimating the state of charge of a batteryin accordance with some embodiments.

FIG. 8 shows experiment results by applying least square optimization tothe method mentioned in the disclosure in accordance with someembodiments.

FIG. 9 shows experiment results by applying the method 100 of estimatingthe state of charge of a battery in FIG. 1 in accordance with someembodiments.

FIG. 10 is a flow chart of a method estimating the state of charge of abattery in accordance with some embodiments.

FIG. 11 is a flow chart of a method of estimating the state of charge ofa battery based on the battery voltage in accordance with someembodiments.

FIG. 12 is a block diagram of a system of estimating the state of chargeof a battery based on the battery voltage in accordance with someembodiments.

FIG. 13 is a block diagram of an exemplary block diagram of a method ofestimating the state of charge of a battery in accordance with someembodiments.

FIG. 14 is a block diagram of a system of estimating the state of charge(SOC) of a battery based on the battery voltage in accordance with someembodiments.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the provided subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present disclosure. These are, of course, merelyexamples and are not intended to be limiting. For example, the formationof a first feature over or on a second feature in the description thatfollows may include embodiments in which the first and second featuresare formed in direct contact, and may also include embodiments in whichadditional features may be formed between the first and second features,such that the first and second features may not be in direct contact. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.

The invention relates to a method for estimating the state of charge(SOC) of a battery when battery is in the at least states of: charging,discharging, and relaxing. The invention makes use of the batteryvoltage (VBAT) instead of the battery current. In order to build modelsin the method, we use standard charging and discharging processes tocollect battery information. For example, we apply different chargingand discharging currents to observe the SOC and the battery voltage(VBAT). Accordingly, based on the observation, we build a membershipfunction (or relationship) between (1) the difference between thebattery voltage (VBAT) and an open circuit voltage (OCV) of the battery;and (2) a SOC difference to be used to adjust the estimated SOC.Furthermore, based on the observation, we generate another membershipfunction (or relationship) between a weighting (or gain) to be appliedto the SOC difference and the battery voltage (VBAT). The two membershipfunctions forms a general model, which can be optimized accordingspecific battery charging and discharging information. The specificbattery data is usually the most frequent usage in user experiences.Additionally, we can find an optimized gain (K) by using minimized leastsquare error method to further tune the SOC difference. The establishedmodels for example can be stored in a hardware memory or written into asoftware program.

In the embodiment, a method of estimating the state of charge (SOC) of abattery is proposed. The method includes: monitoring the battery voltage(VBAT); and estimating the SOC based on a first battery model, a secondbattery model, and the battery voltage. The first battery model includesa first predetermined relationship between the battery voltage and afirst weight based on battery information collected by charging anddischarging and relaxing the battery. The second battery model includesa second predetermined relationship between the difference between thebattery voltage and an estimated open circuit voltage of the battery,and a SOC difference based on the battery information.

FIG. 1 is a block diagram of an exemplary block diagram of a method ofestimating the state of charge of a battery in accordance with someembodiments. As shown in FIG. 1, a battery voltage estimation method 100is provided. The method 100 includes a weighting fuzzifier 110, adSOC/dV fuzzifier 120, a multiplier 125, an optimizer 130, anaccumulator 140, and an open circuit voltage (OCV) lookup table 150.Each block in the diagram can be processed by hardware such as acircuit, or software such as a program executed by a microprocessor, orfirmware.

The method 100 monitors a battery voltage (VBAT), e.g., by a voltagemeasurement or detector circuit. The weighting fuzzifier 110 estimates afirst weight 112 based on the first battery model and the batteryvoltage VBAT. The dSOC/dV fuzzifier 120 estimates a SOC difference(dSOC*) 122 based on the second battery model and the difference 121between the battery voltage VBAT and the estimated open circuit voltage152 of the battery. The multiplier 125 generates a weighted SOCdifference (dSOC) 131 based on the first weight 112 and the SOCdifference (dSOC*) 122. In some embodiments, the optimizer 130 can applyadditional gain (K value) to the weighted SOC difference (dSOC) 131 foroptimization. Next, the accumulator 140 accumulates the weighted SOCdifference (dSOC) 131 by using, for example, the inverse Ztransformation to determine an estimated SOC. The estimated SOC is thenfed back through the OCV lookup table 150 to generate the estimated opencircuit voltage 152, and the process iterates. We will introduce thedetails of the method 100 in the following.

FIG. 2 is a measurement result for building the weighting fuzzifier anddSOC/dV fuzzifier in the method of estimating the state of charge of abattery in accordance with some embodiments. FIG. 2 includes two plots210, 220 which are measured before the real-time estimation of the SOCin the method 100.

The plot 210 demonstrates the measurement result of the relationshipbetween the battery voltage VBAT and the SOC with different chargingconditions. The charging condition OCV refers to charging 2% batterycapacity per hour; the charging condition 0.5 C means charging 50%battery capacity per hour; and the charging condition 0.25 C tellscharging 25% battery capacity per hour. The plot 210 reveals that thegreater the charging rate, the greater the battery voltage VBAT at thesame SOC value.

The plot 220 demonstrates the measurement result of the relationshipbetween the battery voltage VBAT and the SOC with different dischargingconditions. The discharging condition OCV means discharging 2% batterycapacity per hour. The discharging condition 0.5 C refers to discharging50% battery capacity per hour. The discharging condition 0.25 Cindicates discharging 25% battery capacity per hour. The dischargingcondition 0.15 C tells discharging 15% battery capacity per hour. Thedischarging condition 0.1 C refers to discharging 10% battery capacityper hour. The plot 220 reveals that the greater the discharging rate,the lower the battery voltage VBAT at the same SOC value. Next, we willmove on to building the dSOC/dV fuzzifier 120 in FIG. 1.

FIG. 3 shows one part of the model establishment for the dSOC/dVfuzzifier 120 in accordance with some embodiments. FIG. 3 includestables 310, 320 and plots 330, 340. The table 310 contains theinformation extracted from the plots 210 in FIG. 2. For example, withthe same 80% SOC, the battery voltage is 4000 mV for charging 2% batterycapacity per hour, and the battery voltage is 4179 mV for charging 25%battery capacity per hour. Moreover, with the same 60% SOC, the batteryvoltage is 3850 mV for charging 2% battery capacity per hour, and thebattery voltage is 4023 mV for charging 25% battery capacity per hour.

The table 320 is produced based on the information in the table 310. Forexample, with the same 80% SOC, we take OCV (charging 2% batterycapacity per hour) as a basis. The difference between the batteryvoltage for OCV and that for charging 25% battery capacity per hour is179 mV calculated from 4179 mV-4000 mV. Meanwhile, with the same 60%SOC, the difference between the battery voltage for OCV and that forcharging 25% battery capacity per hour is 173 mV calculated from 4023mV-3850 mV. By iterating such calculating process, we can obtain thetable 320. Furthermore, based on the table 320, we can find therelationship 330 between the voltage difference and the charging rate atdifferent SOCs. By normalizing the relationship 330, a curve 340 isgenerated.

FIG. 4 shows another part of the model establishment for the dSOC/dVfuzzifier 120 in FIG. 1 in accordance with some embodiments. FIG. 4includes tables 410, 420 and plots 430, 440. The table 410 contains theinformation extracted from the plots 220 in FIG. 2. For example, withthe same 80% SOC, the battery voltage is 4000 mV for discharging 2%battery capacity per hour, and the battery voltage is 3964 mV fordischarging 10% battery capacity per hour. Moreover, with the same 60%SOC, the battery voltage is 3850 mV for discharging 2% battery capacityper hour, and the battery voltage is 3795 mV for discharging 10% batterycapacity per hour.

The table 420 is produced based on the information in the table 410. Forexample, with the same 80% SOC, we take OCV (discharging 2% batterycapacity per hour) as a basis. The difference between the batteryvoltage for OCV and that for discharging 10% battery capacity per houris 36 mV calculated from 4000 mV-3964 mV. Meanwhile, with the same 60%SOC, the difference between the battery voltage for OCV and that fordischarging 25% battery capacity per hour is 55 mV calculated from 3850mV-3795 mV. By iterating such calculating process, we can obtain thetable 420. Furthermore, based on the table 420, we can find therelationship 430 between the voltage difference and the discharging rateat different SOCs. By normalizing the relationship 430, a curve 440 isgenerated.

FIG. 5 shows an exemplary model of the dSOC/dV fuzzifier 120 in FIG. 1in accordance with some embodiments. By combining the curves 340, 440,we build up the second battery model 510 for the dSOC/dV fuzzifier 120in FIG. 1. Such exemplary model 510 shows that the greater the absolutevalue of the difference (dV) between the battery voltage for OCV andthat for charging/discharging condition, the greater thecharging/discharging current (corresponding to the SOC difference(dSOC*) in FIG. 1), resulting in a V-shape relationship.

FIG. 6 shows the model establishment for the weighting fuzzifier 110 inFIG. 1 in accordance with some embodiments. FIG. 6 includes tables 610,620 and plots 630, 640. The table 610 contains the information extractedfrom the plots 220 in FIG. 2. For example, with the same 90% SOC, thebattery voltage is 4100 mV for discharging 2% battery capacity per hour,and the battery voltage is 4065 mV for discharging 10% battery capacityper hour. Moreover, with the same 80% SOC, the battery voltage is 4000mV for discharging 2% battery capacity per hour, and the battery voltageis 3952 mV for discharging 15% battery capacity per hour. Additionally,with the same 70% SOC, the battery voltage is 3900 mV for discharging 2%battery capacity per hour, and the battery voltage is 3811 mV fordischarging 25% battery capacity per hour.

The table 620 is produced based on the information in the table 610. Forexample, with the same 90% SOC, we take OCV (discharging 2% batterycapacity per hour) as a basis. The weighting for VBAT 4.1V anddischarging 10% battery capacity per hour is 0.29 calculated from10/(4100−4065). The weighting for VBAT 4.0V and discharging 15% batterycapacity per hour is 0.31 calculated from 15/(4000−3952). The weightingfor VBAT 3.9V and discharging 25% battery capacity per hour is 0.28calculated from 25/(3900−3811). By iterating such calculating process,we can obtain the table 620. Furthermore, based on the table 620, we canfind the relationship 630 between the battery voltage (VBAT) and thefirst weight 112 (in FIG. 1) at discharging current. By normalizing therelationship 630, the first model 640 for the weighting fuzzifier 110 inFIG. 1 is generated.

FIG. 7 shows a block diagram and an exemplary data table of an exemplaryblock diagram of a method of estimating the state of charge of a batteryin accordance with some embodiments. As shown in FIG. 7, we recite thebattery voltage estimation method 100 and incorporate the second batterymodel 510 in FIG. 5 and the first battery model 640 in FIG. 6 into themethod 100. Due to the battery's experiencing a discharging condition,the plot 440, which is one part of the second battery model 510, isrecited. Moreover, we provide an exemplary data table 710 for the nodesin the method 100.

In the first battery model 640, we can see that depending upon the VBAT,the first weighting 112 may be between 0.8 and 1.8. In this embodiment,at a VBAT of 3.894 Volts, the first weighting 112 that will be appliedto the output of the Fuzzifier (dSOC/dV) block is 0.9.

The dSOC/dV fuzzifier 120 takes the difference (dV) 121 as its input.The battery voltage VBAT minus the estimated open circuit voltage 152 ofthe battery leaves the difference (dV) 121. The input to the OCV lookuptable 150 is the SOC that is calculated by the method 100. As seen inthis exemplary dSOC/dV fuzzifier 120, the greater the absolute value ofdV 121 the greater the absolute value of the SOC difference (dSOC*) 122output by the dSOC/dV fuzzifier 120. In the plot 440, it shows that atthe difference (dV) 121 of −100 mV, for example, the SOC difference(dSOC*) 122 is −0.25.

As stated earlier the dSOC* calculated by the dSOC/dV fuzzifier 120 isweighted by the output of the weighting fuzzifier 110 and optimized inthe optimizer 130. In some embodiments, the optimizer 130 weights andthen, using least square optimization and the actual charge/dischargedata of the battery, generates a K value that is used to calculate dSOC.

The method 100 then uses dSOC summed with the accumulator 140 (forexample, the inverse Z transformation of SOC) to determine a new SOCvalue. The new SOC value is then fed back through the OCV lookup table150 and the process iterates. An exemplary data table 710 shows thevalues in an exemplary battery showing 3 samples, one every 36 seconds.As can be seen from the above description of the method 100, it operatesby determining the differential voltage and acting upon that using aplurality of fuzzy methods.

FIG. 8 shows experiment results applying least square optimization tothe method mentioned in the disclosure in accordance with someembodiments. As shown in FIG. 8, a method 810 is similar to the method100 in FIG. 1 but with additional least square optimization block 812.The corresponding battery voltage VBAT and SOC are respectively shown inplots 820, 830. The least square optimization block 812 receives theideal SOC in the plot 830 measured by an external testing equipment andan estimated SOC in the plot 830 provided by the method 810. And theleast square optimization block 812 gradually tunes an optimizer 816accordingly. It is shown that based on the tuning conducted by the leastsquare optimization block 812, different weights (or gains) #1-#3 areapplied to the optimizer 816. It turns out that gain #1 has the betterresults among these three and is therefore selected as an optimized gainK.

FIG. 9 shows experiment results by applying the method 100 of estimatingthe state of charge of a battery in FIG. 1 in accordance with someembodiments. FIG. 9 includes three plots 910-930 indicating estimatedSOC errors at different charging/discharging condition. The plot 910shows at 0.5 C standard charging/discharging rate, the estimated SOCerrors is within about −3% to +3%. The plot 920 demonstrates at 0.25 Cstandard charging/discharging rate, the estimated SOC errors is alsowithin about −3% to +3%. The plot 930 reveals at 0.5 C partialcharging/discharging rate, the estimated SOC errors is also within about−4% to +4%. Thus, such plots 910-930 show the accuracy of the method100.

FIG. 10 is a flow chart of a method estimating the state of charge (SOC)of a battery in accordance with some embodiments. A method 1000 isprovided and includes the following operations: monitoring the batteryvoltage (1002); and estimating the SOC based on a first battery model, asecond battery model, and the battery voltage (1004), wherein the firstbattery model includes a first predetermined relationship between thebattery voltage and a first weight based on battery informationcollected by charging and discharging and relaxing the battery, andwherein the second battery model includes a second predeterminedrelationship between the difference between the battery voltage and anestimated open circuit voltage of the battery, and a SOC differencebased on the battery information.

In some embodiments, the operation of monitoring the battery voltagefurther comprises monitoring the battery voltage for a serial multiplebattery cells when the battery in at least one of the states: charging,discharging, and relaxing. In some embodiments, the method 1000 furthercomprises collecting the battery information between the SOC and thebattery voltage at different charging/discharging currents before thereal-time estimation of the SOC. In some embodiments, the operation ofestimating the SOC based on a first battery model, a second batterymodel, and the battery voltage further comprises estimating the SOCwithout monitoring battery current. However, in some other embodimentswherein the battery current information is readily available, thebattery current information can be used to compensate or calibrate theestimation of the SOC, which will be described later.

In some embodiments, the method 1000 further comprises building thefirst battery model and the second battery model by measuring the SOCand the battery voltage at different charging/discharging currents. Insome embodiments, the method 1000 further comprises building the firstbattery model by calculating the first weight using the differencebetween the battery voltage at different charging/discharging currentsand the charging/discharging currents. In some embodiments, the method1000 further comprises building the second battery model by calculatingthe SOC difference using the charging/discharging currents.

In some embodiments, the method 1000 further comprises: estimating thefirst weight based on the first battery model and the battery voltage;estimating the SOC difference based on the second battery model and thedifference between the battery voltage and the estimated open circuitvoltage of the battery; generating a weighted SOC difference based onthe first weight and the SOC difference; accumulating the weighted SOCdifference to provide an estimated SOC; and generating the estimatedopen circuit voltage of the battery based on the estimated SOC and alookup table for the open circuit voltage.

FIG. 11 is a flow chart of a method of estimating the state of charge(SOC) of a battery based on the battery voltage in accordance with someembodiments. A method 1100 is provided and includes the followingoperations: modeling a first predetermined relationship between thebattery voltage and a first weight based on battery informationcollected during charging and discharging to build a first battery model(1102); modeling a second predetermined relationship between thedifference between the battery voltage and an estimated open circuitvoltage of the battery, and a SOC difference based on the batteryinformation (1104); monitoring the battery voltage (1106); andestimating the SOC based on a first battery model, a second batterymodel, and the battery voltage (1108).

In some embodiments, the operation of monitoring the battery voltagefurther comprises monitoring the battery voltage for multiple batterycells in series when the battery is in at least one of the states:charging, discharging, and relaxing. In some embodiments, the method1100 further comprises collecting the battery information between theSOC and the battery voltage at different charging/discharging currentsbefore the real-time estimation of the SOC. In some embodiments, theoperation of estimating the SOC based on a first battery model, a secondbattery model, and the battery voltage further comprises estimating theSOC without monitoring battery current. However, in some otherembodiments wherein the battery current information is readilyavailable, the battery current information can be used to compensate orcalibrate the estimation of the SOC, which will be described later. Insome embodiments, the method 1100 further comprises building the firstbattery model and the second battery model by measuring the SOC and thebattery voltage at different charging/discharging currents.

In some embodiments, the method 1100 further comprises building thefirst battery model by calculating the first weight using the differencebetween the battery voltage at different charging/discharging currentsand the charging/discharging currents. In some embodiments, the method1100 further comprises building the second battery model by calculatingthe SOC difference using the charging/discharging currents.

In some embodiments, the method 1100 further comprises estimating thefirst weight based on the first battery model and the battery voltage;estimating the SOC difference based on the second battery model and thedifference between the battery voltage and the estimated open circuitvoltage of the battery; generating a weighted SOC difference based onthe first weight and the SOC difference; accumulating the weighted SOCdifference to provide an estimated SOC; and generating the estimatedopen circuit voltage of the battery based on the estimated SOC and alookup table for the open circuit voltage.

FIG. 12 is a block diagram of a system of estimating the state of charge(SOC) of a battery based on the battery voltage in accordance with someembodiments. A system 1200 is provided and includes: a first batterymodel 1202 including a first predetermined relationship between thebattery voltage and a first weight based on battery informationcollected by charging and discharging and relaxing the battery; a secondbattery model 1204 including a second predetermined relationship betweenthe difference between the battery voltage and an estimated open circuitvoltage of the battery, and a SOC difference based on the batteryinformation; a voltage detector 1206 monitoring the battery voltage(VBAT); and a SOC estimator 1208 connected to the voltage detector andestimating the SOC based on the first battery model, the second batterymodel, and the battery voltage.

In some embodiments, the voltage detector further monitors the batteryvoltage for a serial multiple battery cells when the battery in at leastone of the states: charging, discharging, and relaxing. In someembodiments, the first battery model and the second battery modelcollects the battery information between the SOC and the battery voltageat different charging/discharging currents before the SOC estimatorstarts real-time estimation of the SOC. In some embodiments, the SOCestimator estimates the SOC without monitoring battery current. However,in some other embodiments wherein the battery current information isreadily available, the battery current information can be used tocompensate or calibrate the estimation of the SOC.

Please refer to FIG. 13, which is a block diagram of an exemplary blockdiagram of a method of estimating the SOC of a battery in accordancewith some embodiments. Each block in the diagram cam be processed byhardware such as a circuit, or software such as a program executed by amicroprocessor, or firmware. In the embodiments shown in FIG. 13, thebattery current information is readily available. As shown in FIG. 13, abattery voltage estimation method 1300 is provided. The method 1300 issimilar to the method 100, but the method 1300 also obtains informationrelated to battery current (IBAT). Preferably but not necessary, thebattery current (IBAT) is multiplied by a current gain 180. The batterycurrent information 182, which in this embodiment is the battery current(IBAT) multiplied by a current gain 180, is sent to a compensator 184.The compensator 184 compensates or calibrates the output of themultiplier 125 to obtain a compensated weighted SOC difference 186. Inone embodiment, the compensator 184 is embodied as a multiplier. Inother embodiments, the compensator 184 can be embodied as an adder, or amore complicated calculation unit performing a calculation to compensateor calibrate the output of the multiplier 125 according to the batterycurrent information. In one embodiment, preferably but not necessarily,the compensated weighted SOC difference 186 is optimized by theoptimizer 130. Thus, in the embodiments shown in FIG. 13, the estimationof the SOC can be adjusted according to the battery current information.Note that the method 1300 is applicable to the methods 1000 and 1100.

Please refer to FIG. 14, which is a block diagram of a system ofestimating the state of charge (SOC) of a battery based on the batteryvoltage in accordance with some embodiments. The system 1400 is similarto the system 1200, except that the SOC estimator 1208 further receivescurrent information, which for example comes from a current sensor 1402.The SOC estimator 1208 is connected to the voltage detector 1206 and thecurrent sensor 1402, and the SOC estimator 1208 estimates the SOC basedon the first battery model, the second battery model, and the batteryvoltage, and compensates the estimation of the SOC according to thebattery current.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

What is claimed is:
 1. A method of estimating the state of charge (SOC)of a battery, comprising: monitoring the battery voltage (VBAT);applying a first weighting to the battery voltage by a first fuzzifier,to obtain a first weight; obtaining a SOC difference between the presentSOC and a previously obtained SOC, and dividing the dSOC by a voltagedifference between the present battery voltage and an open circuitvoltage of the battery by a second fuzzifier, to obtain a derived SOCdifference (dSOC/dV); generating a weighted SOC difference according tothe first weight and the derived SOC difference, by a multiplier; andaccumulating the weighted SOC difference to obtain an estimated SOC, byan accumulator.
 2. The method of claim 1, further comprising: optimizingthe weighted SOC difference by a gain, by an optimizer.
 3. The method ofclaim 1, further comprising: estimating the open circuit voltage of thebattery according to the estimated SOC and a reference table.
 4. Themethod of claim 1, further comprising: applying a second weighting to acurrent of the battery by a gain fuzzifier, to obtain a second weight;and compensating the derived SOC difference by the second weight, by acompensator.
 5. The method of claim 1, wherein monitoring the batteryvoltage further comprises monitoring the battery voltage for multiplebattery cells in series when the battery is in at least one of thestates: charging, discharging, and relaxing.
 6. The method of claim 1,further comprising collecting the battery information between the SOCand the battery voltage at different charging/discharging currentsbefore real-time estimation of the SOC.
 7. The method of claim 1,further comprising building the first battery model and the secondbattery model by measuring the SOC and the battery voltage at differentcharging/discharging currents.
 8. The method of claim 7, furthercomprising building the first battery model by calculating the firstweight using the difference between the battery voltage at differentcharging/discharging currents and the charging/discharging currents. 9.The method of claim 8, further comprising building the second batterymodel by calculating the SOC difference using the charging/dischargingcurrents.
 10. A system of estimating the state of charge (SOC) of abattery based on a battery voltage, comprising: a memory or a softwareprogram storing a first battery model including a first predeterminedrelationship between the battery voltage and a first weight based onbattery information collected by charging and discharging and relaxingthe battery, and a second battery model including a second predeterminedrelationship between the difference between the battery voltage and anestimated open circuit voltage of the battery, and a SOC differencebased on the battery information; a voltage detector monitoring thebattery voltage (VBAT); a current sensor providing information of acurrent of the battery; and a SOC estimator connected to the voltagedetector and the current sensor, the SOC estimator estimating the SOCbased on the first battery model, the second battery model, and thebattery voltage to obtain an estimation of the SOC, and compensating theestimation of the SOC according to the information of the current of thebattery.
 11. The system of claim 10, wherein the voltage detectorfurther monitors the battery voltage for a serial multiple battery cellswhen the battery in at least one of the states: charging, discharging,and relaxing.
 12. The system of claim 11, wherein the first batterymodel and the second battery model collects the battery informationbetween the SOC and the battery voltage at differentcharging/discharging currents before the SOC estimator starts real-timeestimation of the SOC.